On the Inequity of Predicting A While Hoping for B

Author:

Mullainathan Sendhil1,Obermeyer Ziad2

Affiliation:

1. Chicago Booth ()

2. UC Berkeley Public Health ()

Abstract

Algorithms trained to predict mismeasured proxy variables can reproduce and scale up racial bias. This mechanism of algorithmic bias is distinct from others in the literature and harder to detect. We show this using examples from health care, but the forces we consider apply to a range of other important social sectors.

Publisher

American Economic Association

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge;Proceedings of The ACM Collective Intelligence Conference;2023-11-05

2. Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review;Health Affairs;2023-10-01

3. Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022;PLOS Digital Health;2023-09-11

4. Resampling for Mitigating Bias in Predictive Model for Substance Use Disorder Treatment Completion;2023 IEEE 11th International Conference on Healthcare Informatics (ICHI);2023-06-26

5. Counterfactual Prediction Under Outcome Measurement Error;2023 ACM Conference on Fairness, Accountability, and Transparency;2023-06-12

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